The segmentation network has a U-Net architecture with four down-sampling and four respective up-sampling blocks (based on the original U-Net from Ronneberger et al [1]}) with some small adjustments: batchnormalization layer after each non-linear activation, higher dropout rates, ELU activation instead of RELU, Dice loss function, data augmentation and no resampling to force the network to learn different physical representations to increase the generalisation of the network.
